Search

Due to the discrepancy of the high energy demand for rapidly increasing milk production and limited feed intake in the transition period around parturition, dairy cows require considerable metabolic adaptations. We hypothesize that some cows are genetically less suited to cope with these metabolic needs than others, leading to adverse follow-up effects on longevity. To test this, we designed a reaction norm model in which functional lifetime was linked to the metabolic challenge in the beginning of the first lactation. As challenge variables, we used either the sum of milk yield or the accumulated fat-to-protein ratio of the first 3 test-days (

Longevity of dairy cows is determined by culling. Previous studies have shown that culling of dairy cows is not an unambiguous trait but rather the result of several reasons including diseases and selection decisions. The relative importance of these reasons is not stable over time, implying that genetic background of culling may vary over lifetime. Data of 7.6 million German Holstein cows were used to assess the detailed genetic correlation structure among 18 survival traits defined for the first 3 parities. Differences of genetic factors which determine survival of different production periods were found, showing a pattern with 3 genetically distinct periods within each parity: early lactation (calving until d 59), mid lactation (d 60 to 299), and late lactation (d 300 until next calving). Survival in first and later parities were found to be slightly genetically different from each other. The identified patterns were in good accordance with distributions of reasons for disposal, and correlations of estimated breeding values of survival traits for different periods to production and functional traits were generally plausible compared with literature regarding effects on the risk of culling. The study shows that genetic background of survival is variable not only across but also within parities. The results of the study can help developing more accurate models for routine genetic evaluations of longevity that account for nonunity genetic correlations between survival of different periods.

This study presents an approach combining phenotypes from novel traits, deterministic equations from cattle nutrition, and stochastic simulation techniques from animal breeding to generate test-day methane emissions (MEm) of dairy cows. Data included test-day production traits (milk yield, fat percentage, protein percentage, milk urea nitrogen), conformation traits (wither height, hip width, body condition score), female fertility traits (days open, calving interval, stillbirth), and health traits (clinical mastitis) from 961 first lactation Brown Swiss cows kept on 41 low-input farms in Switzerland. Test-day MEm were predicted based on the traits from the current data set and 2 deterministic prediction equations, resulting in the traits labeled MEm1 and MEm2. Stochastic simulations were used to assign individual concentrate intake in dependency of farm-type specifications (requirement when calculating MEm2). Genetic parameters for MEm1 and MEm2 were estimated using random regression models. Predicted MEm had moderate heritabilities over lactation and ranged from 0.15 to 0.37, with highest heritabilities around DIM 100. Genetic correlations between MEm1 and MEm2 ranged between 0.91 and 0.94. Antagonistic genetic correlations in the range from 0.70 to 0.92 were found for the associations between MEm2 and milk yield. Genetic correlations between MEm with days open and with calving interval increased from 0.10 at the beginning to 0.90 at the end of lactation. Genetic relationships between MEm2 and stillbirth were negative (0 to -0.24) from the beginning to the peak phase of lactation. Positive genetic relationships in the range from 0.02 to 0.49 were found between MEm2 with clinical mastitis. Interpretation of genetic (co)variance components should also consider the limitations when using data generated by prediction equations. Prediction functions only describe that part of MEm which is dependent on the factors and effects included in the function. With high probability, there are more important effects contributing to variations of MEm that are not explained or are independent from these functions. Furthermore, autocorrelations exist between indicator traits and predicted MEm. Nevertheless, this integrative approach, combining information from dairy cattle nutrition with dairy cattle genetics, generated novel traits which are difficult to record on a large scale. The simulated data basis for MEm was used to determine the size of a cow calibration group for genomic selection. A calibration group including 2,581 cows with MEm phenotypes was competitive with conventional breeding strategies.

The aim of this study was to answer the question whether models for genetic evaluations of longevity should include a correction for age at first calving (AFC). For this purpose, phenotypic and genetic relationships between AFC, its component traits age at first insemination (AFI) and interval from first to last insemination (FLI), and survival of different periods of the first lactation (S1: 0 to 49 d, S2: 50 to 249 d, S3: 250 d to second calving) were investigated. Data of 721,919 German Holstein heifers, being inseminated for the first time during the years from 2003 to 2012, were used for the analyses. Phenotypic correlations of AFI, FLI, and AFC to S1 to S3 were negative. Mean estimated heritabilities were 0.239 (AFI), 0.007 (FLI), and 0.103 (AFC) and 0.023 (S1), 0.016 (S2), and 0.028 (S3) on the observed scale. The genetic correlation between AFI and FLI was close to zero. Genetic correlations between AFI and the survival traits were -0.08 (S1), -0.02 (S2), and -0.10 (S3); those between FLI and the survival traits were -0.14 (S1), -0.20 (S2), and -0.44 (S3); and those between AFC and the survival traits were -0.09 (S1), -0.06 (S2), and -0.20 (S3). Some of these genetic correlations were different from zero, which suggests that correcting for AFC in genetic evaluations for longevity in dairy cows might remove functional genetic variance and should be reconsidered.

The aim of this study was to estimate genetic parameters and accuracies of breeding values for a set of functional, behavior, and conformation traits in Brown Swiss cattle. These traits were milking speed, udder depth, position of labia, rank order in herd, general temperament, aggressiveness, milking temperament, and days to first heat. Data of 1,799 phenotyped Brown Swiss cows from 40 Swiss dairy herds were analyzed taking the complete pedigree into account. Estimated heritabilities were within the ranges reported in literature, with results at the high end of the reported values for some traits (e.g., milking speed: 0.42±0.06, udder depth: 0.42±0.06), whereas other traits were of low heritability and heritability estimates were of low accuracy (e.g., milking temperament: 0.04±0.04, days to first heat: 0.02±0.04). For most behavior traits, we found relatively high heritabilities (general temperament: 0.38±0.07, aggressiveness: 0.12±0.08, and rank order in herd: 0.16±0.06). Position of labia, arguably an indicator trait for pathological urovagina, was genetically analyzed in this study for the first time, and a moderate heritability (0.28±0.06) was estimated.

Several arguments exist for breeding organizations to focus on cooperative herds for progeny testing, but an efficient methodology addressing herd selection strategies is lacking. In this study, a new approach based on yield deviations (YD) to identify the most informative cooperator herds in terms of genetic differentiation was evaluated. Data comprised YD from 717,377 first-lactation cows from 2 regions in East and West Germany calving between January 2003 and January 2008. Daughters were ranked and classified within sire according to their YD for protein yield, fat yield, milk yield, and somatic cell score. Cows in created YD classes were merged with respective herd-calving year (HCY) characteristics. Cows of extreme YD classes (i.e., such classes including the most extreme daughter contributions), belonged to herds characterized by a high HCY production level, a low value for HCY somatic cell count, and a low HCY age at first calving (AFC). Cows with low extremes for YD in protein yield were associated with the lowest HCY production level, a high value for HCY somatic cell count, and a late HCY AFC. Ranks of HCY and ranks of herds considering HCY over the whole analyzed period were calculated by averaging YD percentages within HCY, and within herds, respectively. The YD percentages (in absolute values so that negative and positive daughter contributions were treated equally) were derived from the rank of the YD of a daughter within sire in relation to all daughters of a sire. A further partitioning of ranks of herds into quartiles revealed the following results: herds in the first quartile had the highest average protein yield, the highest intra-herd standard deviation for the national production index, and the lowest AFC. Correlations between herd rankings for different production traits ranged between 0.64 and 0.86, and were 0.65 for West Germany and 0.62 for East Germany between HCY 2006 and the average herd rank of all calving years. Correlations between daughter yield deviations for the highest and the lowest herd quartile of 0.87 for protein yield disproved concerns regarding genotype by environment interaction between test and production environment. The suggested methodology to identify informative cooperator herds is easy to implement, holds for regions with small herd sizes, and thus, may help in implementing sustainable and competitive dairy cattle breeding programs.

Genetic diversity within livestock species is threatened by extinction of breeds and by genetic drift; the need to conserve genetic diversity by conservation schemes is widely accepted. To maintain the between-breed diversity, breeds are usually kept separately in live conservation schemes. However, in some cases it might be very difficult or even impossible to conserve a highly endangered breed in a closed population. If this breed is important for diversity, it might be beneficial to merge it with one or more breeds to conserve a part of the diversity that is contributed by this breed. The present study introduces a general framework that may enable one to decide when it is beneficial to form a synthetic breed that includes highly endangered breeds to maximize conserved diversity and when to keep the breeds separate. Expected future diversities were estimated using a kinship-based diversity measure together with extinction probabilities of the breeds. Using a small hypothetical data set, the pattern of diversity and its 2 components, within-breed and between-breed diversity, were analyzed in detail when forming a synthetic breed. The suggested approach was applied to a data set of 13 central European red and yellow cattle breeds. The results suggested forming a synthetic breed by combining a nonendangered breed with 1 of the 2 highly endangered breeds, which would result in a slight increase in conserved diversity.

The primary aim of this study was to estimate variance components for traits related to embryo transfer (ET) by applying generalized linear mixed models (GLMM) for different distributions of traits (normal, binomial, and Poisson) in a synergistic context. Synergistic models were originally developed for traits affected by several genotypes, denoted as maternal, paternal, and direct effects. In the case of ET, the number of flushed ova (FO) only depends on a donor's maternal genetic effect, whereas paternal fertility must be considered for other embryo survival traits, such as the number of transferable embryos (TE), the number of degenerated embryos (DE), the number of unfertilized oocytes (UO), and the percentage of transferable embryos (PTE). Data for these traits were obtained from 4,196 flushes of 2,489 Holstein cows within 4 regions of northwest Germany from January 1998 through October 2004. Estimates of maternal heritability were 0.231 for FO, 0.096 for TE, 0.021 for DE, 0.135 for UO, and 0.099 for PTE, whereas the relative genetic impact of the paternal component was near zero. Estimates of the genetic correlations between the maternal and the paternal component were slightly negative, indicating a genetic antagonism. For the analysis of pregnancy after ET, 8,239 transfers to 6,819 different Holstein-Friesian recipients were considered by applying threshold methodology. The direct heritability for pregnancy in the recipient after ET was 0.056. The relative genetic impact of maternal and paternal components on pregnancy of recipients describing a donor's and a sire's ability to produce viable embryos was below 1%. The genetic correlations of the direct effect of the recipient with the sire of embryos (paternal effect) and the donor cow (maternal effect) for pregnancy after ET were -0.32 and -0.14, respectively. With the exception of FO and PTE (-0.17), estimates of genetic correlations among traits for the maternal site were distinctly positive, especially between FO and TE (0.74). Based on this high genetic correlation and due to the higher heritability for FO, indirect selection on FO will increase selection response in TE by about 22% compared with direct selection on TE. The negative genetic correlation of -0.27 between TE and lactation milk yield indicates the need for development of an index for bull dams in multiple ovulation and embryo transfer (MOET) breeding schemes combining production as well as traits related to ET.

National estimated breeding values of bulls from 1998 through 2006 from 12 different German artificial insemination (AI) organizations were used to determine the differences in expected and realized selection intensities for cow sire selection, considering the total merit index as well as subindexes for production, conformation, somatic cell count, fertility, and functional herd life. The expected selection intensity was derived from a Gaussian distribution and from the replacement rate describing the percentage of bulls graduated as cow sires from the total amount of progeny-tested young bulls within the AI organization and by birth year. Realized selection intensities for all indexes were derived from the selection differential of cow sires, defined as the deviation of the average index of selected cow sires from the average index of the total number of progeny-tested young bulls. A low replacement rate of cow sires was associated with relatively high realized selection intensities for the total merit, production, and conformation indexes, but was not related to the somatic cell count, fertility, and functional herd life indexes. The controlling value, defined as the ratio of realized to expected selection intensities, indicates the effectiveness of cow sire selection for different traits. Low controlling values (i.e., low realized selection intensities in combination with moderate or high expected selection intensities) suggest improvements in the step of cow sire selection, especially when discussing the total merit index. Analysis of variance revealed significant differences in expected selection intensities, realized selection intensities, and controlling values for the total merit, production, and conformation indexes between AI organizations and birth years of bulls. Artificial insemination organizations applying well-defined breeding policies (e.g., high controlling values for the total merit index) were successful in the national competition when evaluated according to the national top lists for the respective indexes, regardless of the active population size. The suggested method also allows for comparison of the importance of different indexes in selection decisions. Furthermore, controlling values can monitor additional potential in the improvement of cow sire selection with respect to improvement of the genetic level in the whole population. The development of appropriate selection tools or controlling instruments is of increasing concern for monitoring selection policies in the short term as well as for establishing sustainable breeding policies.

Measurements for average milk flow (AMF) in kilograms of milk per minute of milking time from 629,161 Holstein cows from calving years 1990 to 2008 were used to estimate genetic covariance components using a variety of statistical models. For bivariate linear-threshold model applications, Gaussian-distributed AMF (linear sire model) was categorized into 2 distinct classes (threshold sire model) by setting arbitrary thresholds for extremely slow or extremely fast milking cows. In different bivariate runs with the 2 traits, Gaussian AMF and binary AMF, within a Bayesian framework, thresholds for the binary trait were 1.2, 1.6, 2.6, and 2.8 kg/min. Posterior heritabilities for AMF from the linear and the threshold models in all runs were in a narrow range and close to 0.26, and the posterior genetic correlation between AMF, defined as either a Gaussian or binary trait, was 0.99. A data subset was used to infer genetic and phenotypic relationships between AMF with test-day traits milk yield, fat percentage, protein percentage, somatic cell score (SCS), fat-to-protein ratio, and energy-corrected milk using recursive linear sire models, standard multiple trait linear sire models, and multiple trait linear sire models accounting for the effect of a trait 1 on a trait 2, and of trait 2 on trait 3, via linear regressions. The time-lagged 3-trait system focused on the first test-day trait after calving (trait 1), on AMF (trait 2), and on the test-day trait (trait 3) after the AMF measurement. Posterior means for heritabilities for AMF from linear and recursive linear models used for the reduced data set ranged between 0.29 and 0.38, and were slightly higher than heritabilities from the threshold models applied to the full data set. Genetic correlations from the recursive linear model and the linear model were similar for identical trait combinations including AMF and test-day traits 1 and 3. The largest difference was found for the genetic correlation between AMF and fat percentage from the first test day (i.e., -0.31 from the recursive linear model vs. -0.26 from the linear model). Genetic correlations from the linear model, including an additional regression coefficient, partly differed, especially when comparing correlations between AMF and SCS and between AMF and fat-to-protein ratio recorded after the AMF measurement data. Structural equation coefficients from the recursive linear model and corresponding regression coefficients from the linear model with additional regression, both depicting associations on the phenotypic scale, were quite similar. From a physiological perspective, all models confirmed the antagonistic relationship between SCS with AMF on genetic and phenotypic scales. A pronounced recursive relationship was also noted between productivity (milk yield and energy-corrected milk) and AMF, suggesting further research using physiological parameters as indicators for cow stress response (e.g., level of hormones) should be conducted.

Milking frequencies measured at official test days were used with repeated measurement analysis to reveal the environmental and genetic impact on the milking frequency of cows in automatic milking systems. Repeated measurements were 3 test-day observations per cow within days in milk (DIM) classes, with 1,216 cows in DIM class 1 (d 0 to 99), from 1,112 cows in DIM class 2 (d 100 to 199), and from 1,004 cows in DIM class 3 (d 200 to 299) kept in 15 farms. Selection criteria for models analyzing repeated measurements were Akaike and Schwarz Bayesian values, which favored the autoregressive [AR(1)] covariance structure over the compound symmetry model. Results from the AR(1) model indicated a significant impact of fixed herd and parity effects. Milking frequencies decreased with increasing parities and were greatest for first-parity cows. High daily milk yield was associated with higher milking frequencies. Heritabilities for milking frequency were 0.16, 0.19, and 0.22 in DIM classes 1, 2, and 3, respectively, from the AR(1) model. Higher heritabilities in the later stage of lactation were due to a substantial reduction of the residual variance. Genetic correlations between test-day milk yield and daily milking frequency were in the range of 0.46 to 0.57 for all DIM classes and between milking frequency and somatic cell score were near zero. For verification of results, milking frequencies of the same cows obtained from herd management programs were averaged within DIM classes. Heritabilities were slightly above the values from the AR(1) model. In conclusion, heritabilities for milking frequency in automatic milking systems are moderate enough to incorporate this behavioral trait in a combined breeding goal. The inevitable improvement of labor efficiency in dairy cattle farming demands such cows going easily and voluntarily in automatic milking systems.

Because of the relatively high levels of genetic relationships among potential bull sires and bull dams, innovative selection tools should consider both genetic gain and genetic relationships in a long-term perspective. Optimum genetic contribution theory using official estimated breeding values for a moderately heritable trait (production index, Index-PROD), and a lowly heritable functional trait (index for somatic cell score, Index-SCS) was applied to find optimal allocations of bull dams and bull sires. In contrast to previous practical applications using optimizations based on Lagrange multipliers, we focused on semi-definite programming (SDP). The SDP methodology was combined with either pedigree ( a ij ) or genomic relationships ( f ij ) among selection candidates. Selection candidates were 484 genotyped bulls, and 499 preselected genotyped bull dams completing a central test on station. In different scenarios separately for PROD and SCS, constraints on the average pedigree relationships among future progeny were varied from a ij =0.08 to a ij =0.20 in increments of 0.01. Corresponding constraints for single nucleotide polymorphism-based kinship coefficients were derived from regression analysis. Applying the coefficient of 0.52 with an intercept of 0.14 estimated for the regression pedigree relationship on genomic relationship, the corresponding range to alter genomic relationships varied from f ij = 0.18 to f ij = 0.24. Despite differences for some bulls in genomic and pedigree relationships, the same trends were observed for constraints on pedigree and corresponding genomic relationships regarding results in genetic gain and achieved coefficients of relationships. Generally, allowing higher values for relationships resulted in an increase of genetic gain for Index-PROD and Index-SCS and in a reduction in the number of selected sires. Interestingly, more sires were selected for all scenarios when restricting genomic relationships compared with restricting pedigree relationships. For example, at constraint of f ij =0.185 and selection on Index-PROD, the number of selected sires was 35. In contrast, only 21 sires were selected at the comparable constraint on additive genetic relationship of a ij =0.09. A further reduction in relationships is possible when using SDP output (i.e., suggested genetic contributions of selected parents) and applying a simulated annealing algorithm to define specific mating plans. However, the advantage of this strategy is limited to a short-term perspective and probably not successful in the period of genomic selection allowing a substantial reduction of generation intervals.

Data included observations on more than 200,000 first lactations of Norwegian cattle. Milk yield, fat and protein percentages, and observations on mastitis, ketosis, and presence of disease (binary coding of 0 or 1) were analyzed. Following Bayesian principles and applying the threshold concept, dispersion parameters for the binary traits (on the underlying scale) with continuous production traits were estimated. Heritabilities were .27, .34, and .43 for milk yield, fat percentage, and protein percentage, respectively. Heritabilities for the disease traits were .05 to .13 but may be inconsistent because of methodology problems with small sire by herd-year-season subclass size. Genetic correlations between milk yield and all three disease traits were above .5, indicating an undesirable relationship. Genetic correlations between ketosis and the content traits were –.38 to –.65; low component percentages were associated with high ketosis frequencies. Ignoring diseases in breeding programs may lead to undesirable correlated selection responses when selecting on milk yield.

The aims of the study were to evaluate the relationships among milk urea nitrogen and nonreturn rates at the phenotypic scale, and to estimate genetic parameters among milk urea nitrogen, milk yield, and fertility traits in the early period of lactation. Milk yield, protein percentage, the interval from calving to first service, and 56- and 90-d nonreturn rates were available from 73,344 Holstein cows from 2,178 different herds located in a region in northwestern Germany. Generalized linear models with a logit link function were applied to assess the phenotypic relationships. Bivariate threshold-threshold, linear-threshold, and linear-linear models, fitted in a Bayesian framework, were used to estimate genetic correlations among traits. Milk yield, protein percentage, and milk urea nitrogen were means from test-day 1 (on average 20.8 d in milk) and test-day 2 (on average 53.1 d in milk) after calving. An increase in milk urea nitrogen was associated with decreasing 56-d nonreturn rates on the phenotypic scale. At fixed levels of milk urea nitrogen, greater values of protein percentage, indicating a surplus of energy in the feed, were positively associated with nonreturn rates. Heritabilities were 0.03 for 56- and 90-d nonreturn rates, 0.07 for interval from calving to first service, 0.13 for milk urea nitrogen, and 0.19 for milk yield. Service sire explained a negligible part (below 0.15%) of the total variance for nonreturn rates. Genetic correlations between the interval from calving to first service and nonreturn rates were close to zero. The genetic correlation between nonreturn rates was 0.94, suggesting that a change from nonreturn after 90 d to nonreturn after 56 d in the national genetic evaluation would not result in any loss of information. The genetic correlation between milk yield and nonreturn after 56 d was -0.31, and between milk yield and calving to first service was 0.14, both indicating an antagonistic relationship between production and reproduction. The genetic correlation between milk yield and milk urea nitrogen was 0.44, reflecting an energy deficiency in early lactation. The genetic correlations between milk urea nitrogen and nonreturn rates were too weak (-0.19 for 56-d nonreturn rate, and -0.23 for 90-d nonreturn rate) to justify the use of milk urea nitrogen as an additional trait in genetic selection for fertility, as demonstrated by selection index calculations.

Relationships between claw disorders and test-day milk yield recorded in 2005 on 5,360 Holstein cows, kept on 11 large-scale dairy farms in eastern Germany, were analyzed in a Bayesian framework with standard linear and threshold models and recursive linear and threshold models. Four different claw disorders, digital dermatitis (DD), sole ulcer (SU), wall disorder (WD), and interdigital hyperplasia (IH), were scored as binary traits within 200 d after calving and analyzed separately. Incidences of disorders were 13.7% for DD, 16.5% for SU, 9.8% for WD, and 6.7% for IH. Heritabilities of disorders were greater when applying threshold or recursive threshold models than with linear or linear recursive models. Posterior means of genetic correlations between test-day milk production and claw disorders ranged from 0.17 to 0.44, suggesting that breeding strategies focusing on increased milk yield will increase incidences of disorders as a correlated response. A progressive path of lagged relationships was postulated for recursive models describing first the influence of test-day milk yield (MY1) on claw disorders and second, the effect of the disorder on milk production level at the following test day (MY2). In recursive models, structural coefficients describe recursive relationships at the phenotypic level. The structural coefficient lambda21 was the gradient of disease (trait 2) with respect to MY1 (trait 1) for a model with a recursive effect of trait 1 on trait 2. The increase of disease incidence of the 4 different disorders per 1-kg increase of MY1 ranged from lambda21 = 0.006 to lambda21 = 0.024 on the visible scale when applying recursive linear models, and from lambda21 = 0.003 to lambda21 = 0.016 on the underlying liability scale for recursive threshold models. The rate of change in MY2 (trait 3) with respect to the previous claw disorder is given by lambda32 for a model with a recursive effect from trait 2 to trait 3. Structural coefficients lambda32 ranged from -0.12 to -0.68 predicting that a 1-unit increase in the incidence of any disorder reduces milk yield at the following test day by up to 0.67 kg. Rank correlations between sire posterior means for the same claw disorders among different models were >0.84, but some changes in rank of sires in distinct top-10 lists were observed. Structural equation models are of increasing importance in genetic evaluations, and this study showed the possible application of recursive systems, even for categorical data.

The primary aim of this study was to estimate heritabilities for different types of claw and foot disorders and the genetic relationship of disorders with milk yield and selected conformation traits by applying logistic models in Holstein dairy cattle. The study included data from 5634 Holstein cows kept on large-scale dairy farms in Eastern Germany. Dichotomous response variables were the presence or absence of the disorder in 2003. Cows that were present in herds for